
Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG)
- 1st Edition - January 16, 2022
- Imprint: Academic Press
- Authors: Archana Bajirao Kanwade, Vinayak Bairagi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 0 5 0 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 8 6 0 8 - 6
Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) presents a new and innovative method of COPD diagnosis using EMG to analyze sternomas… Read more

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Request a sales quoteChronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) presents a new and innovative method of COPD diagnosis using EMG to analyze sternomastoid muscle activity using features extraction and classification. The book describes the methodology of EMG analysis, the slope-based onset detection algorithm and SEMG analysis in time, frequency and time frequency domain analyses. It also explores the identification of frequencies for single frequency Continuous Wavelet Transform (CWT) analysis and feature extraction and selection for successful classification COPD into its severity grades.
The book provides a compilation of all techniques used in the literatures and emphasizes newly proposed techniques for the early detection of COPD. Fully comprehensive, the book includes discussion of limitations of existing methods for COPD diagnosis and introduces new efficient methods for COPD identification, classification and early diagnosis.
- Provides an easy, simple and comprehensive guide to using EMG analysis for COPD diagnosis
- Presents detailed explanations of the recently developed slope-based onset detection algorithm for muscle activity detection, along with numerous original figures, tables and graphs to aid interpretation
- Includes a complete review of various features, such as extraction using single frequency CWT analysis and the feature selection algorithm for COPD diagnosis
Researchers, academics and scientists working in the field of Respiratory diseases, Biomedical Engineering, EMG signal processing and Pulmonologists. Biomedical Signal Processing Course specifically for the EMG signal Processing details. Respiratory medicine branch for Chronic Obstructive Pulmonary Disease subject
- Cover image
- Title page
- Table of Contents
- Copyright
- About the author
- Preface
- Acknowledgments
- Chapter 1. Introduction
- Abstract
- 1.1 Chronic Obstructive Pulmonary Disease (COPD)
- 1.2 Chronic Obstructive Pulmonary Disease burden
- 1.3 Current techniques for assessing chronic obstructive pulmonary disease
- 1.4 Respiratory muscles and mechanics of respiration
- 1.5 Role of sternomastoid muscle in chronic obstructive pulmonary disease
- 1.6 Electromyography
- 1.7 Need for research
- 1.8 Summary
- References
- Chapter 2. Methodology
- Abstract
- 2.1 Introduction
- 2.2 Methodology
- 2.3 Recording techniques
- 2.4 Skin preparations
- 2.5 Electromyography affecting factors
- 2.6 Electromyography filtering and associated noise
- 2.7 Electromyography analysis
- 2.8 Feature selection and classification
- 2.9 Summary
- References
- Chapter 3. Chronic Obstructive Pulmonary Disease and healthy classification
- Abstract
- 3.1 Introduction
- 3.2 System information
- 3.3 Time domain analysis
- 3.4 Onset detection algorithm
- 3.5 Feature selection
- 3.6 Classification algorithm
- 3.7 Results
- 3.8 Summary
- References
- Chapter 4. Chronic Obstructive Pulmonary Disease grade classification
- Abstract
- 4.1 Introduction
- 4.2 Frequency domain analysis
- 4.3 Time-frequency domain analysis
- 4.4 Feature selection algorithm
- 4.5 Classification of Chronic Obstructive Pulmonary Disease grades
- 4.6 Results
- 4.7 Summary
- References
- Chapter 5. Early detection of Chronic Obstructive Pulmonary Disease
- Abstract
- 5.1 Introduction
- 5.2 Literature survey
- 5.3 Early diagnosis model
- 5.4 Early COPD diagnosis results
- 5.5 Summary
- References
- Chapter 6. Future scope and application
- Abstract
- 6.1 Conclusion
- 6.2 Future scope and applications
- Index
- Edition: 1
- Published: January 16, 2022
- No. of pages (Paperback): 192
- No. of pages (eBook): 192
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323900508
- eBook ISBN: 9780323886086
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Archana Bajirao Kanwade
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